Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations280790
Missing cells382536
Missing cells (%)6.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.3 MiB
Average record size in memory169.0 B

Variable types

Numeric14
Categorical6
DateTime1
Boolean1

Alerts

BAIXO_PESO is highly overall correlated with GESTACAO and 2 other fieldsHigh correlation
CONSPRENAT is highly overall correlated with CONSULTAS and 1 other fieldsHigh correlation
CONSULTAS is highly overall correlated with CONSPRENAT and 2 other fieldsHigh correlation
GESTACAO is highly overall correlated with BAIXO_PESO and 1 other fieldsHigh correlation
KOTELCHUCK is highly overall correlated with CONSPRENAT and 1 other fieldsHigh correlation
MESPRENAT is highly overall correlated with CONSULTASHigh correlation
PARTO is highly overall correlated with STCESPARTOHigh correlation
PESO is highly overall correlated with BAIXO_PESOHigh correlation
QTDFILVIVO is highly overall correlated with QTDPARTNORHigh correlation
QTDPARTNOR is highly overall correlated with QTDFILVIVOHigh correlation
RACACOR is highly overall correlated with RACACORMAEHigh correlation
RACACORMAE is highly overall correlated with RACACORHigh correlation
SEMAGESTAC is highly overall correlated with BAIXO_PESO and 1 other fieldsHigh correlation
STCESPARTO is highly overall correlated with PARTOHigh correlation
GRAVIDEZ is highly imbalanced (92.3%)Imbalance
BAIXO_PESO is highly imbalanced (57.0%)Imbalance
ESCMAE2010 has 3803 (1.4%) missing valuesMissing
CONSPRENAT has 5240 (1.9%) missing valuesMissing
MESPRENAT has 7911 (2.8%) missing valuesMissing
QTDPARTNOR has 13723 (4.9%) missing valuesMissing
QTDPARTCES has 15290 (5.4%) missing valuesMissing
STCESPARTO has 30253 (10.8%) missing valuesMissing
SEMAGESTAC has 4521 (1.6%) missing valuesMissing
GESTACAO has 4365 (1.6%) missing valuesMissing
SEXO has 251000 (89.4%) missing valuesMissing
RACACORMAE has 10457 (3.7%) missing valuesMissing
RACACOR has 8859 (3.2%) missing valuesMissing
QTDFILVIVO has 9755 (3.5%) missing valuesMissing
QTDFILMORT has 14981 (5.3%) missing valuesMissing
QTDPARTCES is highly skewed (γ1 = 28.34834527)Skewed
QTDFILMORT is highly skewed (γ1 = 29.90289762)Skewed
QTDPARTNOR has 171113 (60.9%) zerosZeros
QTDPARTCES has 189675 (67.6%) zerosZeros
QTDFILVIVO has 114210 (40.7%) zerosZeros
QTDFILMORT has 213857 (76.2%) zerosZeros

Reproduction

Analysis started2025-09-30 18:20:17.034375
Analysis finished2025-09-30 18:21:02.718112
Duration45.68 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

PESO
Real number (ℝ)

High correlation 

Distinct3224
Distinct (%)1.1%
Missing83
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3177.8499
Minimum100
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2025-09-30T18:21:02.868189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile2240
Q12900
median3210
Q33520
95-th percentile3995
Maximum9999
Range9899
Interquartile range (IQR)620

Descriptive statistics

Standard deviation563.1771
Coefficient of variation (CV)0.17721954
Kurtosis3.2091692
Mean3177.8499
Median Absolute Deviation (MAD)310
Skewness-0.89115248
Sum8.9204471 × 108
Variance317168.45
MonotonicityNot monotonic
2025-09-30T18:21:02.997517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32002478
 
0.9%
30002449
 
0.9%
33002387
 
0.9%
31002309
 
0.8%
34002203
 
0.8%
35002079
 
0.7%
32501853
 
0.7%
36001831
 
0.7%
31501806
 
0.6%
33501788
 
0.6%
Other values (3214)259524
92.4%
ValueCountFrequency (%)
1001
< 0.1%
1051
< 0.1%
1101
< 0.1%
1231
< 0.1%
1301
< 0.1%
1511
< 0.1%
1551
< 0.1%
1591
< 0.1%
1851
< 0.1%
2001
< 0.1%
ValueCountFrequency (%)
99992
< 0.1%
68401
< 0.1%
68051
< 0.1%
67811
< 0.1%
65501
< 0.1%
65002
< 0.1%
63991
< 0.1%
63001
< 0.1%
62002
< 0.1%
60651
< 0.1%

IDADEMAE
Real number (ℝ)

Distinct53
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean26.910159
Minimum11
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2025-09-30T18:21:03.120517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile17
Q121
median27
Q332
95-th percentile38
Maximum99
Range88
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.7508597
Coefficient of variation (CV)0.25086658
Kurtosis-0.60371717
Mean26.910159
Median Absolute Deviation (MAD)5
Skewness0.24802832
Sum7555996
Variance45.574107
MonotonicityNot monotonic
2025-09-30T18:21:03.243259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2314101
 
5.0%
2214086
 
5.0%
2113944
 
5.0%
2513784
 
4.9%
2613720
 
4.9%
2713643
 
4.9%
2413593
 
4.8%
2813547
 
4.8%
2013454
 
4.8%
2913027
 
4.6%
Other values (43)143887
51.2%
ValueCountFrequency (%)
116
 
< 0.1%
1248
 
< 0.1%
13404
 
0.1%
141660
 
0.6%
153734
 
1.3%
166368
2.3%
178449
3.0%
1810406
3.7%
1912125
4.3%
2013454
4.8%
ValueCountFrequency (%)
992
< 0.1%
631
< 0.1%
622
< 0.1%
611
< 0.1%
601
< 0.1%
591
< 0.1%
572
< 0.1%
561
< 0.1%
552
< 0.1%
542
< 0.1%

ESCMAE2010
Real number (ℝ)

Missing 

Distinct7
Distinct (%)< 0.1%
Missing3803
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean3.0783575
Minimum0
Maximum9
Zeros1233
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2025-09-30T18:21:03.338009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1370102
Coefficient of variation (CV)0.36935612
Kurtosis2.9441273
Mean3.0783575
Median Absolute Deviation (MAD)0
Skewness0.99134221
Sum852665
Variance1.2927922
MonotonicityNot monotonic
2025-09-30T18:21:03.417077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3141849
50.5%
263383
22.6%
544122
 
15.7%
414010
 
5.0%
110976
 
3.9%
91414
 
0.5%
01233
 
0.4%
(Missing)3803
 
1.4%
ValueCountFrequency (%)
01233
 
0.4%
110976
 
3.9%
263383
22.6%
3141849
50.5%
414010
 
5.0%
544122
 
15.7%
91414
 
0.5%
ValueCountFrequency (%)
91414
 
0.5%
544122
 
15.7%
414010
 
5.0%
3141849
50.5%
263383
22.6%
110976
 
3.9%
01233
 
0.4%

CONSULTAS
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing113
Missing (%)< 0.1%
Memory size2.1 MiB
4.0
198120 
3.0
59953 
2.0
 
15941
1.0
 
5194
9.0
 
1469

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters842031
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
4.0198120
70.6%
3.059953
 
21.4%
2.015941
 
5.7%
1.05194
 
1.8%
9.01469
 
0.5%
(Missing)113
 
< 0.1%

Length

2025-09-30T18:21:03.507312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-30T18:21:03.583877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4.0198120
70.6%
3.059953
 
21.4%
2.015941
 
5.7%
1.05194
 
1.9%
9.01469
 
0.5%

Most occurring characters

ValueCountFrequency (%)
.280677
33.3%
0280677
33.3%
4198120
23.5%
359953
 
7.1%
215941
 
1.9%
15194
 
0.6%
91469
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)842031
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.280677
33.3%
0280677
33.3%
4198120
23.5%
359953
 
7.1%
215941
 
1.9%
15194
 
0.6%
91469
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)842031
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.280677
33.3%
0280677
33.3%
4198120
23.5%
359953
 
7.1%
215941
 
1.9%
15194
 
0.6%
91469
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)842031
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.280677
33.3%
0280677
33.3%
4198120
23.5%
359953
 
7.1%
215941
 
1.9%
15194
 
0.6%
91469
 
0.2%

CONSPRENAT
Real number (ℝ)

High correlation  Missing 

Distinct48
Distinct (%)< 0.1%
Missing5240
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean8.5709998
Minimum0
Maximum99
Zeros1642
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2025-09-30T18:21:03.693956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q16
median8
Q310
95-th percentile13
Maximum99
Range99
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.2351981
Coefficient of variation (CV)0.84414868
Kurtosis124.90158
Mean8.5709998
Median Absolute Deviation (MAD)2
Skewness10.231781
Sum2361739
Variance52.348092
MonotonicityNot monotonic
2025-09-30T18:21:04.312476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
840167
14.3%
1039218
14.0%
936226
12.9%
735036
12.5%
628108
10.0%
518804
6.7%
1115914
 
5.7%
1214677
 
5.2%
412672
 
4.5%
38082
 
2.9%
Other values (38)26646
9.5%
ValueCountFrequency (%)
01642
 
0.6%
12780
 
1.0%
24968
 
1.8%
38082
 
2.9%
412672
 
4.5%
518804
6.7%
628108
10.0%
735036
12.5%
840167
14.3%
936226
12.9%
ValueCountFrequency (%)
991440
0.5%
771
 
< 0.1%
751
 
< 0.1%
691
 
< 0.1%
641
 
< 0.1%
631
 
< 0.1%
422
 
< 0.1%
4110
 
< 0.1%
4033
 
< 0.1%
3943
 
< 0.1%

MESPRENAT
Real number (ℝ)

High correlation  Missing 

Distinct11
Distinct (%)< 0.1%
Missing7911
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean5.1011584
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2025-09-30T18:21:04.416628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile6
Maximum99
Range98
Interquartile range (IQR)1

Descriptive statistics

Standard deviation15.65202
Coefficient of variation (CV)3.0683266
Kurtosis31.738727
Mean5.1011584
Median Absolute Deviation (MAD)1
Skewness5.7829172
Sum1391999
Variance244.98574
MonotonicityNot monotonic
2025-09-30T18:21:04.498999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2103790
37.0%
160543
21.6%
353353
19.0%
422827
 
8.1%
512456
 
4.4%
997318
 
2.6%
66295
 
2.2%
73452
 
1.2%
81815
 
0.6%
91007
 
0.4%
(Missing)7911
 
2.8%
ValueCountFrequency (%)
160543
21.6%
2103790
37.0%
353353
19.0%
422827
 
8.1%
512456
 
4.4%
66295
 
2.2%
73452
 
1.2%
81815
 
0.6%
91007
 
0.4%
1023
 
< 0.1%
ValueCountFrequency (%)
997318
 
2.6%
1023
 
< 0.1%
91007
 
0.4%
81815
 
0.6%
73452
 
1.2%
66295
 
2.2%
512456
 
4.4%
422827
 
8.1%
353353
19.0%
2103790
37.0%

KOTELCHUCK
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4341465
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2025-09-30T18:21:04.574739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median5
Q35
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5964881
Coefficient of variation (CV)0.36004406
Kurtosis1.5638664
Mean4.4341465
Median Absolute Deviation (MAD)0
Skewness0.47956809
Sum1245064
Variance2.5487744
MonotonicityNot monotonic
2025-09-30T18:21:04.664489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5175001
62.3%
250146
 
17.9%
420052
 
7.1%
319604
 
7.0%
914345
 
5.1%
11642
 
0.6%
ValueCountFrequency (%)
11642
 
0.6%
250146
 
17.9%
319604
 
7.0%
420052
 
7.1%
5175001
62.3%
914345
 
5.1%
ValueCountFrequency (%)
914345
 
5.1%
5175001
62.3%
420052
 
7.1%
319604
 
7.0%
250146
 
17.9%
11642
 
0.6%

GRAVIDEZ
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing320
Missing (%)0.1%
Memory size2.1 MiB
1.0
274366 
2.0
 
5967
3.0
 
126
9.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters841410
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0274366
97.7%
2.05967
 
2.1%
3.0126
 
< 0.1%
9.011
 
< 0.1%
(Missing)320
 
0.1%

Length

2025-09-30T18:21:04.761385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-30T18:21:04.826806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0274366
97.8%
2.05967
 
2.1%
3.0126
 
< 0.1%
9.011
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
.280470
33.3%
0280470
33.3%
1274366
32.6%
25967
 
0.7%
3126
 
< 0.1%
911
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)841410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.280470
33.3%
0280470
33.3%
1274366
32.6%
25967
 
0.7%
3126
 
< 0.1%
911
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)841410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.280470
33.3%
0280470
33.3%
1274366
32.6%
25967
 
0.7%
3126
 
< 0.1%
911
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)841410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.280470
33.3%
0280470
33.3%
1274366
32.6%
25967
 
0.7%
3126
 
< 0.1%
911
 
< 0.1%

QTDPARTNOR
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct26
Distinct (%)< 0.1%
Missing13723
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean0.69567562
Minimum0
Maximum99
Zeros171113
Zeros (%)60.9%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2025-09-30T18:21:04.924268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum99
Range99
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3122063
Coefficient of variation (CV)1.886233
Kurtosis386.12389
Mean0.69567562
Median Absolute Deviation (MAD)0
Skewness7.888942
Sum185792
Variance1.7218854
MonotonicityNot monotonic
2025-09-30T18:21:05.027426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0171113
60.9%
150693
 
18.1%
223801
 
8.5%
310821
 
3.9%
45114
 
1.8%
52589
 
0.9%
61326
 
0.5%
7730
 
0.3%
8386
 
0.1%
9226
 
0.1%
Other values (16)268
 
0.1%
(Missing)13723
 
4.9%
ValueCountFrequency (%)
0171113
60.9%
150693
 
18.1%
223801
 
8.5%
310821
 
3.9%
45114
 
1.8%
52589
 
0.9%
61326
 
0.5%
7730
 
0.3%
8386
 
0.1%
9226
 
0.1%
ValueCountFrequency (%)
993
< 0.1%
581
 
< 0.1%
392
 
< 0.1%
302
 
< 0.1%
232
 
< 0.1%
222
 
< 0.1%
213
< 0.1%
205
< 0.1%
171
 
< 0.1%
161
 
< 0.1%

QTDPARTCES
Real number (ℝ)

Missing  Skewed  Zeros 

Distinct21
Distinct (%)< 0.1%
Missing15290
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean0.37511111
Minimum0
Maximum99
Zeros189675
Zeros (%)67.6%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2025-09-30T18:21:05.125364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum99
Range99
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.78792334
Coefficient of variation (CV)2.1005065
Kurtosis3085.9937
Mean0.37511111
Median Absolute Deviation (MAD)0
Skewness28.348345
Sum99592
Variance0.62082318
MonotonicityNot monotonic
2025-09-30T18:21:05.223179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0189675
67.6%
157204
 
20.4%
215097
 
5.4%
32904
 
1.0%
4476
 
0.2%
567
 
< 0.1%
628
 
< 0.1%
1015
 
< 0.1%
209
 
< 0.1%
74
 
< 0.1%
Other values (11)21
 
< 0.1%
(Missing)15290
 
5.4%
ValueCountFrequency (%)
0189675
67.6%
157204
 
20.4%
215097
 
5.4%
32904
 
1.0%
4476
 
0.2%
567
 
< 0.1%
628
 
< 0.1%
74
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
993
 
< 0.1%
701
 
< 0.1%
411
 
< 0.1%
322
 
< 0.1%
253
 
< 0.1%
231
 
< 0.1%
223
 
< 0.1%
209
< 0.1%
143
 
< 0.1%
112
 
< 0.1%

PARTO
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing213
Missing (%)0.1%
Memory size2.1 MiB
2.0
159557 
1.0
121009 
9.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters841731
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0159557
56.8%
1.0121009
43.1%
9.011
 
< 0.1%
(Missing)213
 
0.1%

Length

2025-09-30T18:21:05.317918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-30T18:21:05.383406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0159557
56.9%
1.0121009
43.1%
9.011
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
.280577
33.3%
0280577
33.3%
2159557
19.0%
1121009
14.4%
911
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)841731
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.280577
33.3%
0280577
33.3%
2159557
19.0%
1121009
14.4%
911
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)841731
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.280577
33.3%
0280577
33.3%
2159557
19.0%
1121009
14.4%
911
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)841731
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.280577
33.3%
0280577
33.3%
2159557
19.0%
1121009
14.4%
911
 
< 0.1%

STCESPARTO
Categorical

High correlation  Missing 

Distinct4
Distinct (%)< 0.1%
Missing30253
Missing (%)10.8%
Memory size2.1 MiB
3.0
95845 
1.0
79117 
2.0
68330 
9.0
 
7245

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters751611
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row9.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.095845
34.1%
1.079117
28.2%
2.068330
24.3%
9.07245
 
2.6%
(Missing)30253
 
10.8%

Length

2025-09-30T18:21:05.461713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-30T18:21:05.531233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.095845
38.3%
1.079117
31.6%
2.068330
27.3%
9.07245
 
2.9%

Most occurring characters

ValueCountFrequency (%)
.250537
33.3%
0250537
33.3%
395845
 
12.8%
179117
 
10.5%
268330
 
9.1%
97245
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)751611
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.250537
33.3%
0250537
33.3%
395845
 
12.8%
179117
 
10.5%
268330
 
9.1%
97245
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)751611
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.250537
33.3%
0250537
33.3%
395845
 
12.8%
179117
 
10.5%
268330
 
9.1%
97245
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)751611
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.250537
33.3%
0250537
33.3%
395845
 
12.8%
179117
 
10.5%
268330
 
9.1%
97245
 
1.0%

SEMAGESTAC
Real number (ℝ)

High correlation  Missing 

Distinct27
Distinct (%)< 0.1%
Missing4521
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean38.45005
Minimum19
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2025-09-30T18:21:05.624861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile35
Q138
median39
Q340
95-th percentile41
Maximum45
Range26
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2330566
Coefficient of variation (CV)0.058076819
Kurtosis10.887931
Mean38.45005
Median Absolute Deviation (MAD)1
Skewness-2.2888255
Sum10622557
Variance4.986542
MonotonicityNot monotonic
2025-09-30T18:21:05.728708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
3980071
28.5%
3858129
20.7%
4052626
18.7%
3726475
 
9.4%
4120394
 
7.3%
3612017
 
4.3%
356582
 
2.3%
424703
 
1.7%
344131
 
1.5%
332548
 
0.9%
Other values (17)8593
 
3.1%
(Missing)4521
 
1.6%
ValueCountFrequency (%)
1929
 
< 0.1%
2045
 
< 0.1%
2179
 
< 0.1%
22122
 
< 0.1%
23159
 
0.1%
24219
0.1%
25234
0.1%
26336
0.1%
27343
0.1%
28470
0.2%
ValueCountFrequency (%)
45345
 
0.1%
44662
 
0.2%
431403
 
0.5%
424703
 
1.7%
4120394
 
7.3%
4052626
18.7%
3980071
28.5%
3858129
20.7%
3726475
 
9.4%
3612017
 
4.3%

GESTACAO
Real number (ℝ)

High correlation  Missing 

Distinct7
Distinct (%)< 0.1%
Missing4365
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean4.8904694
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2025-09-30T18:21:05.808619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median5
Q35
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.45965157
Coefficient of variation (CV)0.093989253
Kurtosis14.412294
Mean4.8904694
Median Absolute Deviation (MAD)0
Skewness-2.5174079
Sum1351848
Variance0.21127956
MonotonicityNot monotonic
2025-09-30T18:21:05.886447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5237782
84.7%
427040
 
9.6%
67114
 
2.5%
32864
 
1.0%
21413
 
0.5%
1154
 
0.1%
958
 
< 0.1%
(Missing)4365
 
1.6%
ValueCountFrequency (%)
1154
 
0.1%
21413
 
0.5%
32864
 
1.0%
427040
 
9.6%
5237782
84.7%
67114
 
2.5%
958
 
< 0.1%
ValueCountFrequency (%)
958
 
< 0.1%
67114
 
2.5%
5237782
84.7%
427040
 
9.6%
32864
 
1.0%
21413
 
0.5%
1154
 
0.1%

SEXO
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing251000
Missing (%)89.4%
Memory size2.1 MiB
1.0
15330 
0.0
14460 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters89370
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.015330
 
5.5%
0.014460
 
5.1%
(Missing)251000
89.4%

Length

2025-09-30T18:21:05.996584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-30T18:21:06.062214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.015330
51.5%
0.014460
48.5%

Most occurring characters

ValueCountFrequency (%)
044250
49.5%
.29790
33.3%
115330
 
17.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)89370
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
044250
49.5%
.29790
33.3%
115330
 
17.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)89370
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
044250
49.5%
.29790
33.3%
115330
 
17.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)89370
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
044250
49.5%
.29790
33.3%
115330
 
17.2%

RACACORMAE
Real number (ℝ)

High correlation  Missing 

Distinct6
Distinct (%)< 0.1%
Missing10457
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean2.8234548
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2025-09-30T18:21:06.117670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q34
95-th percentile4
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.4406854
Coefficient of variation (CV)0.51025624
Kurtosis-1.5506504
Mean2.8234548
Median Absolute Deviation (MAD)0
Skewness-0.33021285
Sum763273
Variance2.0755745
MonotonicityNot monotonic
2025-09-30T18:21:06.196291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4153974
54.8%
195412
34.0%
217046
 
6.1%
52521
 
0.9%
31192
 
0.4%
9188
 
0.1%
(Missing)10457
 
3.7%
ValueCountFrequency (%)
195412
34.0%
217046
 
6.1%
31192
 
0.4%
4153974
54.8%
52521
 
0.9%
9188
 
0.1%
ValueCountFrequency (%)
9188
 
0.1%
52521
 
0.9%
4153974
54.8%
31192
 
0.4%
217046
 
6.1%
195412
34.0%

PARIDADE
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
1
172152 
0
108638 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters280790
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1172152
61.3%
0108638
38.7%

Length

2025-09-30T18:21:06.292344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-30T18:21:06.350793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1172152
61.3%
0108638
38.7%

Most occurring characters

ValueCountFrequency (%)
1172152
61.3%
0108638
38.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)280790
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1172152
61.3%
0108638
38.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)280790
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1172152
61.3%
0108638
38.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)280790
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1172152
61.3%
0108638
38.7%

RACACOR
Real number (ℝ)

High correlation  Missing 

Distinct6
Distinct (%)< 0.1%
Missing8859
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean2.8269083
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2025-09-30T18:21:06.407882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q34
95-th percentile4
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.4401222
Coefficient of variation (CV)0.50943364
Kurtosis-1.5482295
Mean2.8269083
Median Absolute Deviation (MAD)0
Skewness-0.33526501
Sum768724
Variance2.0739519
MonotonicityNot monotonic
2025-09-30T18:21:06.488186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4155202
55.3%
195715
34.1%
217075
 
6.1%
52553
 
0.9%
31198
 
0.4%
9188
 
0.1%
(Missing)8859
 
3.2%
ValueCountFrequency (%)
195715
34.1%
217075
 
6.1%
31198
 
0.4%
4155202
55.3%
52553
 
0.9%
9188
 
0.1%
ValueCountFrequency (%)
9188
 
0.1%
52553
 
0.9%
4155202
55.3%
31198
 
0.4%
217075
 
6.1%
195715
34.1%

QTDFILVIVO
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct21
Distinct (%)< 0.1%
Missing9755
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean1.0269264
Minimum0
Maximum99
Zeros114210
Zeros (%)40.7%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2025-09-30T18:21:06.587576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum99
Range99
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3201982
Coefficient of variation (CV)1.2855821
Kurtosis343.14506
Mean1.0269264
Median Absolute Deviation (MAD)1
Skewness6.5097291
Sum278333
Variance1.7429234
MonotonicityNot monotonic
2025-09-30T18:21:06.684800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0114210
40.7%
188625
31.6%
240271
 
14.3%
315424
 
5.5%
46368
 
2.3%
53031
 
1.1%
61468
 
0.5%
7784
 
0.3%
8419
 
0.1%
9214
 
0.1%
Other values (11)221
 
0.1%
(Missing)9755
 
3.5%
ValueCountFrequency (%)
0114210
40.7%
188625
31.6%
240271
 
14.3%
315424
 
5.5%
46368
 
2.3%
53031
 
1.1%
61468
 
0.5%
7784
 
0.3%
8419
 
0.1%
9214
 
0.1%
ValueCountFrequency (%)
993
 
< 0.1%
301
 
< 0.1%
202
 
< 0.1%
171
 
< 0.1%
161
 
< 0.1%
154
 
< 0.1%
147
 
< 0.1%
136
 
< 0.1%
1223
< 0.1%
1151
< 0.1%

QTDFILMORT
Real number (ℝ)

Missing  Skewed  Zeros 

Distinct17
Distinct (%)< 0.1%
Missing14981
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean0.24927297
Minimum0
Maximum99
Zeros213857
Zeros (%)76.2%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2025-09-30T18:21:06.774383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum99
Range99
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.64313564
Coefficient of variation (CV)2.5800456
Kurtosis4198.619
Mean0.24927297
Median Absolute Deviation (MAD)0
Skewness29.902898
Sum66259
Variance0.41362345
MonotonicityNot monotonic
2025-09-30T18:21:06.861836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0213857
76.2%
141574
 
14.8%
27900
 
2.8%
31784
 
0.6%
4423
 
0.2%
5135
 
< 0.1%
669
 
< 0.1%
732
 
< 0.1%
810
 
< 0.1%
109
 
< 0.1%
Other values (7)16
 
< 0.1%
(Missing)14981
 
5.3%
ValueCountFrequency (%)
0213857
76.2%
141574
 
14.8%
27900
 
2.8%
31784
 
0.6%
4423
 
0.2%
5135
 
< 0.1%
669
 
< 0.1%
732
 
< 0.1%
810
 
< 0.1%
94
 
< 0.1%
ValueCountFrequency (%)
992
 
< 0.1%
161
 
< 0.1%
151
 
< 0.1%
141
 
< 0.1%
122
 
< 0.1%
115
 
< 0.1%
109
 
< 0.1%
94
 
< 0.1%
810
 
< 0.1%
732
< 0.1%

DT_NASC
Date

Distinct3507
Distinct (%)1.3%
Missing1645
Missing (%)0.6%
Memory size2.1 MiB
Minimum2001-01-10 00:00:00
Maximum2024-11-14 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-09-30T18:21:06.986509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:21:07.119530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

BAIXO_PESO
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size274.3 KiB
False
256073 
True
 
24717
ValueCountFrequency (%)
False256073
91.2%
True24717
 
8.8%
2025-09-30T18:21:07.204861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Interactions

2025-09-30T18:20:58.332714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:29.392747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:31.229549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:32.969562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:34.930557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:38.967890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:41.375971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:43.173761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:44.853651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:46.859531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:48.578911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:50.318761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:53.195546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:56.058182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:58.456750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:29.533165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:31.351429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:33.099130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:35.117422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:39.176384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:41.502100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:43.298177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:45.301890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:46.978923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:48.702847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:50.450092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:53.399498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:56.257706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:58.568702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:29.658135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:31.466163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:33.209589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:35.280501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:39.367160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:41.640471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:43.408425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:45.412637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:47.094110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:48.823916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:50.576763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:53.600652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:56.450026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:58.698531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:29.795140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:31.588171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:33.319471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:35.442072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:39.553409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:41.759682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:43.519724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:45.527816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:47.228273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:48.947987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:50.698222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:53.791807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:56.634967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:58.810255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:29.925385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:31.705591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:33.436071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:35.616024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:39.747687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:41.885248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:43.652018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:45.638567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:47.344866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:49.078101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:50.825881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:53.983233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:56.827021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:58.933245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:30.049700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:31.842093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:33.551950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:35.784662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:39.936304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:42.007003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:43.767377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:45.768907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:47.464713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:49.196161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:51.039912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:54.186825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:57.010170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:59.099742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:30.186371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:31.964691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:33.675801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:35.976179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:40.151403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:42.136647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:43.892574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:45.891756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:47.592551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:49.329748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:51.224537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:54.395798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:57.214221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:59.385620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:30.309953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:32.081204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:33.791767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:36.160481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:40.343584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:42.267357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:44.006808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:46.005257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:47.711988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:49.442792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:51.401906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:54.598465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:57.402985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:59.498333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:30.441971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:32.201881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:33.921358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:36.341853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:40.556608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:42.390516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:44.120101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:46.119801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:47.843633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:49.561965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:51.578079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:54.794624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:57.621300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:59.620875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:30.561349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:32.318129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:34.040344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:36.518584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:40.747807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:42.513750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:44.238561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:46.241726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:47.964908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:49.677336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:51.751157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:54.997718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:57.741954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:59.760086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:30.693358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:32.445459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:34.209322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:36.717091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:40.887521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:42.666503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:44.353348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:46.352371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:48.081916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:49.805872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:51.925156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:55.213021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:57.860224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:59.883622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:30.837408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:32.592233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:34.393666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:36.924789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:41.012825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:42.792931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:44.477337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:46.476289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:48.209216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:49.940998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:52.129249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:55.435499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:57.985506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:21:00.005093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:30.969502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:32.724542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:34.576246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:37.156940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:41.140325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:42.921930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:44.607593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:46.606027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:48.341514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:50.065735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:52.799378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:55.651276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:58.111521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:21:00.118091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:31.096963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:32.852361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:34.758208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:37.355018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:41.261252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:43.052284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:44.739697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:46.721434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:48.460716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:50.191486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:52.984577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:55.853028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-30T18:20:58.223975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-30T18:21:07.284828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
BAIXO_PESOCONSPRENATCONSULTASESCMAE2010GESTACAOGRAVIDEZIDADEMAEKOTELCHUCKMESPRENATPARIDADEPARTOPESOQTDFILMORTQTDFILVIVOQTDPARTCESQTDPARTNORRACACORRACACORMAESEMAGESTACSEXOSTCESPARTO
BAIXO_PESO1.0000.0840.1260.0140.5220.2770.0340.1240.0280.0300.0170.6740.0040.0040.0000.0000.0170.0170.5380.0200.028
CONSPRENAT0.0841.0000.5430.2270.1400.0170.1700.620-0.4010.0520.0730.1150.013-0.1460.015-0.167-0.181-0.1810.1110.0000.061
CONSULTAS0.1260.5431.0000.1190.0940.0190.0730.5960.5310.0840.1140.0780.0000.0160.0030.0180.0850.0850.0940.0090.092
ESCMAE20100.0140.2270.1191.000-0.0070.0230.2850.183-0.2420.1770.1740.008-0.036-0.2800.026-0.319-0.254-0.254-0.0830.0030.144
GESTACAO0.5220.1400.094-0.0071.0000.137-0.0280.0690.0090.0120.0470.333-0.0270.0060.0030.0010.0180.0180.6150.0080.031
GRAVIDEZ0.2770.0170.0190.0230.1371.0000.1260.0150.0200.0110.3900.1520.0010.0060.0000.0050.0140.0140.1390.0150.046
IDADEMAE0.0340.1700.0730.285-0.0280.1261.0000.125-0.1620.3520.2040.0430.1880.3800.2630.215-0.156-0.156-0.1080.0000.121
KOTELCHUCK0.1240.6200.5960.1830.0690.0150.1251.000-0.4920.0790.1140.0560.010-0.1120.012-0.132-0.104-0.1110.0400.0140.097
MESPRENAT0.028-0.4010.531-0.2420.0090.020-0.162-0.4921.0000.0140.042-0.009-0.0170.134-0.0280.1620.1570.1570.0600.0030.061
PARIDADE0.0300.0520.0840.1770.0120.0110.3520.0790.0141.0000.0240.0800.0070.0220.0090.0240.0640.0640.0330.0000.039
PARTO0.0170.0730.1140.1740.0470.3900.2040.1140.0420.0241.0000.0520.0000.0130.0000.0150.1120.1120.0790.0140.703
PESO0.6740.1150.0780.0080.3330.1520.0430.056-0.0090.0800.0521.000-0.0020.0910.0670.0470.0170.0160.3860.0970.044
QTDFILMORT0.0040.0130.000-0.036-0.0270.0010.1880.010-0.0170.0070.000-0.0021.0000.1790.1120.1700.0190.019-0.0390.0000.000
QTDFILVIVO0.004-0.1460.016-0.2800.0060.0060.380-0.1120.1340.0220.0130.0910.1791.0000.4730.7170.1160.116-0.0050.0040.010
QTDPARTCES0.0000.0150.0030.0260.0030.0000.2630.012-0.0280.0090.0000.0670.1120.4731.000-0.204-0.046-0.046-0.0610.0000.000
QTDPARTNOR0.000-0.1670.018-0.3190.0010.0050.215-0.1320.1620.0240.0150.0470.1700.717-0.2041.0000.1650.1650.0360.0030.012
RACACOR0.017-0.1810.085-0.2540.0180.014-0.156-0.1040.1570.0640.1120.0170.0190.116-0.0460.1651.0001.0000.0750.0000.121
RACACORMAE0.017-0.1810.085-0.2540.0180.014-0.156-0.1110.1570.0640.1120.0160.0190.116-0.0460.1651.0001.0000.0730.0000.121
SEMAGESTAC0.5380.1110.094-0.0830.6150.139-0.1080.0400.0600.0330.0790.386-0.039-0.005-0.0610.0360.0750.0731.0000.0160.079
SEXO0.0200.0000.0090.0030.0080.0150.0000.0140.0030.0000.0140.0970.0000.0040.0000.0030.0000.0000.0161.0000.000
STCESPARTO0.0280.0610.0920.1440.0310.0460.1210.0970.0610.0390.7030.0440.0000.0100.0000.0120.1210.1210.0790.0001.000

Missing values

2025-09-30T18:21:00.295270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-30T18:21:00.896321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-09-30T18:21:02.305895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

PESOIDADEMAEESCMAE2010CONSULTASCONSPRENATMESPRENATKOTELCHUCKGRAVIDEZQTDPARTNORQTDPARTCESPARTOSTCESPARTOSEMAGESTACGESTACAOSEXORACACORMAEPARIDADERACACORQTDFILVIVOQTDFILMORTDT_NASCBAIXO_PESO
03000.018.04.03.06.03.041.00.00.01.0NaN38.05.00.04.004.00.00.02014-01-15False
13994.035.03.03.05.05.021.00.02.02.02.038.05.00.04.014.02.01.02014-02-19False
22820.032.01.03.06.03.041.01.0NaN1.0NaN38.05.01.0NaN1NaN1.0NaN2014-05-19False
33000.017.02.04.07.03.051.01.0NaN1.0NaN43.06.00.04.014.01.0NaN2014-02-20False
42690.021.04.03.06.02.041.0NaNNaN1.0NaN39.05.00.04.004.0NaNNaN2014-06-10False
52210.018.02.03.06.05.021.00.00.02.02.041.05.00.04.004.00.00.02014-06-30True
62710.029.03.01.0NaN3.091.00.00.02.09.039.05.01.04.004.00.00.02014-04-28False
72880.018.03.03.04.03.031.00.00.01.0NaN36.04.01.04.004.00.00.02014-02-26False
83455.019.03.03.05.099.091.00.01.02.02.037.05.00.01.011.01.00.02015-03-23False
93280.020.02.04.09.05.021.00.00.02.01.040.05.01.04.004.00.00.02014-07-22False
PESOIDADEMAEESCMAE2010CONSULTASCONSPRENATMESPRENATKOTELCHUCKGRAVIDEZQTDPARTNORQTDPARTCESPARTOSTCESPARTOSEMAGESTACGESTACAOSEXORACACORMAEPARIDADERACACORQTDFILVIVOQTDFILMORTDT_NASCBAIXO_PESO
2807802885.031.05.04.07.02.051.00.00.02.01.037.05.0NaN1.001.00.00.02024-03-30False
2807812310.030.05.04.08.01.052.00.02.02.01.034.04.0NaN4.014.02.00.02023-11-23True
2807823205.024.03.04.07.02.051.01.00.01.03.039.05.0NaN4.014.01.00.02023-12-06False
2807832614.033.03.03.04.07.021.00.02.02.01.039.05.0NaN4.014.02.01.02023-09-15False
2807843525.015.03.04.08.03.051.00.00.01.03.041.05.0NaN4.004.00.00.02023-08-25False
2807853230.037.05.04.011.02.051.00.01.02.02.039.05.0NaN4.014.01.00.02023-10-11False
2807863155.025.02.04.08.02.051.00.01.01.03.035.04.0NaN4.014.01.00.02024-01-22False
2807873040.024.05.04.09.02.051.00.01.02.02.038.05.0NaN4.014.01.00.02024-02-05False
2807883410.037.05.04.012.02.051.00.00.02.02.039.05.0NaN1.011.00.01.02023-02-21False
2807893795.026.03.04.010.01.051.01.00.01.03.039.05.0NaN1.011.01.00.02024-03-12False